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RavenK
/
TAC-ViT-base

Feature Extraction
Transformers
PyTorch
Safetensors
clip_vision_model
Model card Files Files and versions
xet
Community

Instructions to use RavenK/TAC-ViT-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use RavenK/TAC-ViT-base with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("feature-extraction", model="RavenK/TAC-ViT-base")
    # Load model directly
    from transformers import AutoTokenizer, AutoModel
    
    tokenizer = AutoTokenizer.from_pretrained("RavenK/TAC-ViT-base")
    model = AutoModel.from_pretrained("RavenK/TAC-ViT-base")
  • Notebooks
  • Google Colab
  • Kaggle
TAC-ViT-base
700 MB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 8 commits
RavenK's picture
RavenK
Update README.md
3bc73cb verified almost 2 years ago
  • .gitattributes
    1.52 kB
    initial commit over 2 years ago
  • README.md
    2.91 kB
    Update README.md almost 2 years ago
  • config.json
    558 Bytes
    Upload model over 2 years ago
  • model.safetensors
    350 MB
    xet
    Upload model over 2 years ago
  • preprocessor_config.json
    546 Bytes
    Upload processor over 2 years ago
  • pytorch_model.bin

    Detected Pickle imports (3)

    • "torch._utils._rebuild_tensor_v2",
    • "torch.FloatStorage",
    • "collections.OrderedDict"

    What is a pickle import?

    350 MB
    xet
    Upload model over 2 years ago